Termplot: A wrapper for `termplot` that optionally (but by default) exponentiates terms, and plot them on a common log-scale. Also scales x-axes to the same physical scale.

Description

The function uses termplot to extract terms from a model with, say, spline, terms, including the standard errors, computes confidence intervals and transform these to the rate / rate-ratio scale. Thus the default use is for models on the log-scale such as Poisson-regression models. The function produces a plot with panels side-by-side, one panel per term, and returns the

Usage

Termplot( obj,
           plot = TRUE,
           xlab = NULL,
           ylab = NULL,
            xeq = TRUE,
           yshr = 1,
          alpha = 0.05,
          terms = NULL,
         max.pt = NULL )

Value

A list with one component per term in the model object obj, each component is a 4-column matrix with $x$ as the first column, and 3 columns with estimae and lower and upper confidence limit.

Arguments

obj: An object with a terms-method --- for details the the documentation for termplot.
plot: Should a plot be produced?
xlab: Labels for the x-axes. Defaults to the names of the terms.
ylab: Labels for the x-axes. Defaults to blank.
xeq: Should the units all all plots have the same physical scale for the x-axes).
yshr: Shrinking of y-axis. By default, the y-axes have an extent that accommodates the entire range of confidence intervals. This is a shrinking parameter for the y-axes, setting it to less than 1 will lose a bit of the confidence limits on some of the panels.
alpha: 1 minus the confidence level for computing confidence intervals
terms: Which terms should be reported. Passed on to termplot and eventually predict.
max.pt: The maximal number of points in which to report the terms. If NULL all unique points from the analysis dataset are reported for each term (this is a feature of termplot).

Author

Bendix Cartensen

Examples

Run this code

# Get the diabetes data and set up as Lexis object
data(DMlate)
DMlate <- DMlate[sample(1:nrow(DMlate),500),]
dml <- Lexis( entry = list(Per=dodm, Age=dodm-dobth, DMdur=0 ),
               exit = list(Per=dox),
        exit.status = factor(!is.na(dodth),labels=c("DM","Dead")),
               data = DMlate )

# Split in 1-year age intervals
dms <- splitLexis( dml, time.scale="Age", breaks=0:100 )

# Model with 6 knots for both age and period
n.kn <- 6
# Model age-specific rates with period referenced to 2004
( a.kn <- with( subset(dms,lex.Xst=="Dead"),
                quantile( Age+lex.dur, probs=(1:n.kn-0.5)/n.kn ) ) )
( p.kn <- with( subset(dms,lex.Xst=="Dead"),
                quantile( Per+lex.dur, probs=(1:n.kn-0.5)/n.kn ) ) )
m2 <- glm( lex.Xst=="Dead" ~ -1 +
                             Ns( Age, kn=a.kn, intercept=TRUE ) +
                             Ns( Per, kn=p.kn, ref=2004 ),
           offset = log( lex.dur ), family=poisson, data=dms )

# Finally we can plot the two effects:
Termplot( m2, yshr=0.9 )

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